We will discuss about “Characterizing possible failure modes in physics-informed neural networks”, Krishnapriyan, Aditi, et al., Advances in Neural Information Processing Systems 34 (2021): 26548-26560. Abstract Recent work in scientific machine learning has developed so-called physics-informed neural network (PINN) models. The typical approach is to incorporate physical domain knowledge as soft constraints on an empirical loss …

We will discuss about “Dynamical information enables inference of gene regulation at single-cell scale”, Zhang, Stephen Y., and Michael PH Stumpf., bioRxiv (2023): 2023-01. Abstract Cellular dynamics and emerging biological function are governed by patterns of gene expression arising from networks of interacting genes. Inferring these interactions from data is a notoriously difficult inverse problem …

We will discuss about “Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks”, Briat, Corentin, Ankit Gupta, and Mustafa Khammash. Cell systems 2.1 (2016): 15-26. Abstract The ability to adapt to stimuli is a defining feature of many biological systems and critical to maintaining homeostasis. While it is well appreciated that negative feedback …

We will discuss about “Single-sample landscape entropy reveals the imminent phase transition during disease progression”, Liu R, Chen P, Chen L., Bioinformatics. 2020 Mar 1;36(5):1522-1532. Abstract Motivation: The time evolution or dynamic change of many biological systems during disease progression is not always smooth but occasionally abrupt, that is, there is a tipping point during …

We will discuss about “The effect of microRNA on protein variability and gene expression fidelity”, Hilfinger, Andreas, and Raymond Fan., Biophysical journal 122.3 (2023): 537a. Abstract Small regulatory RNA molecules such as microRNA modulate gene expression through inhibiting the translation of messenger RNA (mRNA). Such post-transcriptional regulation has been recently hypothesized to reduce the stochastic variability …